International Journal of Artificial Intelligence, Data Science, and Machine Learning
OCF Protocol Implementation for Smart Home IoT Applications: An Android SDK Development Case Study
Findings show that while the interoperability guidelines offered by the Open Connectivity Foundation (OCF) protocol standard appear to offer a framework for devices that can interoperate in different spaces, it is quite clear that engineering issues raised in the implementation of the standard in commercial Android SDK development (device discovery, resource model mapping, security bootstrapping, and cross-platform compatibility) are substantial. The paper gives a detailed case study of an implementation of OCF protocol in terms of implementation of the protocol's resource discovery mechanism in a commercial thermostat smart home product, development of CoAP transport layer integration for OCF, onboarding security flows for the devices, and design of the protocol SDK API to make it accessible for third-party developers. The study clearly reveals architectural constraints faced when translating the specification OCF into Android based libraries, especially on the area of handling constrained device communication and asychronous CoAP messaging for secure credential provisioning using DTLS security layers. The new SDK design is built upon a layered architecture with a transport abstraction layer, OCF resource manager, security bootstrap handler and an application-level API wrapper. Experimental testing in an isolated smart home setup shows better interoperability between multi-vendor devices, lower onboarding time, and higher developer convenience in comparison to the baseline time and tools used by each vendor's proprietary device software development kit (SDK) implementations. Resource optimisation for caching and discovery in a multicast domain shows that the enumeration of all resources in the domain can be reduced without compromising protocol fidelity, substantially. The experience gained in the deployment of production SDK for the Android gives rise to a set of general design principles for OCF-compliant development of Android SDK, such as: light weight CoAP Session reuse, stateless resource mapping techniques, and automated onboarding in a secure manner. The results provide the IoT standards engineering community with workable best practice advice, and well contribute support to the goal of scaling OCF into practical deployment in a commercial Android smart home market.
LLM-Driven Database Administration and Natural Language Query Interfaces
The advent of Large Language Models (LLMs) has transformed multiple domains, including natural language processing, knowledge retrieval, and intelligent automation. In database administration (DBA), LLMs offer unprecedented opportunities to improve efficiency, reduce human error, and enhance accessibility by enabling natural language query interfaces. This paper explores the integration of LLMs into database management systems (DBMS), focusing on their capability to interpret natural language queries, generate complex Structured Query Language (SQL) statements, optimize queries, and autonomously execute administrative tasks. We present a comprehensive methodology for leveraging LLMs as intelligent DBAs, covering query translation, schema understanding, performance optimization, and security enforcement. Furthermore, we examine performance metrics, accuracy evaluations, and user satisfaction through comparative experiments with conventional SQL query interfaces. Our results demonstrate that LLM-driven DBAs significantly reduce query formulation time, increase accessibility for non-technical users, and improve overall database performance while maintaining security compliance. The paper concludes with insights into potential challenges, ethical considerations, and future directions for autonomous database management systems enhanced by LLM technologies.
Survey Analysis on Project Management Practices in Manufacturing and Industrial Operations
Project management is vital to manufacturing and industrial activities in enhancing productivity, operational efficiency, quality control, and project success. This survey paper also presents an analysis of practices of project management adopted in manufacturing industry from the aspect of planning, resource allocation, quality controlling, process improvement and technology integration. The study highlights the project management tools used in Agile Manufacturing, Lean Manufacturing, Six Sigma and the importance of each tool in the following areas: Flexibility, Waste reduction, Quality improvement, Continuous improvement in the industrial field. Additionally, the paper discusses the factors affecting the trend of Industry 4.0 technologies such as Artificial Intelligence (AI), Internet of Things (IoT), automation, smart manufacturing systems and Enterprise Resource Planning (ERP) systems that are improving decision making, operational control, and production efficiency. The survey also highlights key challenges faced by manufacturers in project management, including project implementation costs, employee skill gaps, security risks, technology challenges, and a lack of willingness to embrace digital transformation. The results indicate that the most advanced technologies and best project management practices can be highly valuable in improving operational performance, sustainability and project outcomes. The use of AI-driven and sustainable project management models in the smart manufacturing industry should be investigated further in the future.
A Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making in Software Pipelines
The software development environment is changing quickly into highly distributed, cloud-native and microservices-based architectures. As these types of environments grow, orchestrating, monitoring, scaling, failure recovery, and assuring the integrity of delivery of DevOps pipelines becomes more complex. Traditional DevOps automation tools mainly use static CI/CD pipelines and rule-driven automation scripts that can be inadequate for large-scale, dynamic and heterogeneous deployment environments. In this paper a Multi-Agent AI Framework for Distributed DevOps Automation and Collaborative Decision-Making (MAF-DDACD) is proposed to provide the software delivery pipelines with greater autonomy, intelligence, and resilience. The proposed framework uses multiple specialized AI agents, one for each of the following DevOps tasks: build automation, test orchestration, security scanning, deployment optimization, infrastructure scaling, and anomaly detection. They work together via a shared knowledge layer, and a consensus-based decision engine to get the best result from the pipeline in uncertain and dynamic environments. To enhance adaptability and inter-agent coordination, reinforcement learning (RL), natural language processing (NLP) and graph-based dependency modeling are integrated. It also adds a hierarchical orchestration layer which dynamically assigns tasks to agents based on workload, system health and historical performance metrics. Unlike traditional DevOps systems, MAF-DDACD enables self-healing pipelines, failure prediction, and failure avoidance rollback strategies. The results of the experimental simulation show that the proposed system can be beneficial for deployment efficiency, reduce failure ratio of pipelines, and improve mean-time-to-recovery (MTTR). It delivers up to 32% lower deployment latency, 27% higher pipeline success rate, and 41% quicker response time for detecting anomalies, over traditional CI/CD pipelines. Moreover, the decision-making collaborative mechanism allows agents to reach a consensus on resolving conflicts in a weighted voting and confidence scoring model, which ensures both reliable and explainable automation decisions. The framework can be scaled to hybrid cloud deployments and is open to be integrated with other DevOps tools like kubernetes, jenkins, and terraform. The study concludes that multi-agent AI systems offer a promising direction for future DevOps automation to support intelligent, adaptive, and resilient pipelines for software engineering and support for future scale distributed systems.
AI-Driven Incident Prediction and Self-Healing Infrastructure in Azure Monitor
Cloud-native environments demand continuous reliability, performance, and proactive incident management. Traditional reactive monitoring approaches often result in delayed resolutions and service disruptions. Microsoft Azure Monitor, combined with AI and automation, enables predictive incident detection, intelligent alert correlation, and self-healing infrastructure. This paper explores the architecture and methodologies for implementing AI-driven operational intelligence in Azure. It highlights how Azure Monitor, Log Analytics, and Azure Automation integrate with machine learning to predict failures and trigger autonomous remediation workflows. The study also presents real-world use cases demonstrating measurable improvements in system uptime, mean time to recovery (MTTR), and operational efficiency.
Predictive Validation of Banking APIs and Transaction Workflows Using Machine Learning-Based Defect Detection Model
The global transition toward Open Banking and microservices architectures has exponentially increased the reliance on Application Programming Interfaces (APIs) to drive core financial transaction workflows. As financial institutions move away from monolithic core banking systems toward highly distributed ecosystems, the complexity of verifying inter-service communication has surged. Traditional deterministic software testing methodologies—such as static unit testing and manual regression suites—are increasingly insufficient for identifying complex, edge-case defects within these high-velocity, asynchronous banking environments. This paper proposes a novel predictive validation framework leveraging Machine Learning (ML) to proactively detect defects within API codebases and transaction workflows prior to production deployment. By extracting deep code-level metrics, historical commit logs and workflow dependency graphs, the proposed framework employs an optimized ensemble model, specifically combining Random Forest and Gradient Boosting techniques, to predict the statistical probability of runtime failures. Empirical evaluation utilizing simulated, high-frequency banking telemetry demonstrates that the ML-driven approach identifies defect-prone API modules with an F1-score of 0.89, drastically outperforming legacy rule-based QA systems. Initial architectural designs and workflow simulations suggest that shifting from reactive quality assurance to predictive defect modeling significantly reduces post-deployment API downtime, accelerates the agile software lifecycle and preserves the absolute integrity of critical financial workflows. This research formally bridges the gap between software reliability engineering and advanced decision intelligence within the financial sector.
Application Programming Interfaces (Apis)
Privacy-Preserving Data Engineering in Financial Services: Differential Privacy, Federated Learning, and Secure Computation Models
Financial institutions process highly sensitive personal and transactional data, making privacy protection a critical requirement for modern data platforms. With increasing regulatory scrutiny under frameworks such as GDPR, CCPA, and global financial compliance standards, organizations must adopt advanced techniques that protect customer data while enabling meaningful analytics and machine learning. Traditional approaches such as data masking and tokenization provide partial protection but often limit the usability of datasets for advanced analytics. This paper explores privacy-preserving data engineering techniques that enable secure data processing without exposing sensitive information. The study examines differential privacy mechanisms, federated learning architectures, and secure multi-party computation models as viable approaches for privacy-aware financial analytics. The paper proposes a layered privacy architecture that integrates these techniques within modern financial data pipelines to ensure secure data processing while preserving analytical value. Through architectural analysis and practical implementation considerations, this research demonstrates how financial institutions can balance privacy protection, regulatory compliance, and data-driven innovation
Decision Tree Ensemble Approach for Crop Yield Prediction
This study presents a comprehensive analysis of corn and soybean crop yield determinants on the Maryland Eastern Shore spanning ten growing seasons (2015–2024). A dataset of 2,520 field-season observations, 1,260 corn and 1,260 soybean, across nine counties, was subjected to rigorous descriptive statistical characterization followed by machine learning modeling using a two-tier architecture: a combined Gradient Boosting (GB) model for mixed-crop prediction and interpretation, and crop-specific Random Forest (RF) deployment models for single-crop inference. Corn yields averaged 150.2 bu/ac (SD = 15.1; CV = 10%) and soybean yields averaged 44.0 bu/ac (SD = 8.6; CV = 20%), with soybean exhibiting substantially greater relative variability. The GB combined model achieved near-perfect cross-validated performance (R² = 0.9801, MAE = 5.70 bu/ac, RMSE = 7.68 bu/ac), while crop-specific RF models yielded R² of 0.6835 (corn) and 0.7528 (soybean). Feature attribution analysis identified seeding rate, crop maturity rating, and nitrogen application rate as the dominant agronomic predictors, while June–August precipitation emerged as the primary weather driver, particularly for soybean, where summer moisture governs pod-fill. Partial dependence analysis revealed nonlinear agronomic response curves consistent with established agronomic principles. Together, these findings provide an actionable, data-driven framework for precision crop management in the Mid-Atlantic coastal plain.
Advanced Predictive AI Frameworks for Secure Site Reliability Engineering in Enterprise Systems
The rapid digital transformation of enterprise systems has significantly increased the complexity of maintaining highly available, secure, and resilient infrastructures. Modern enterprises rely on distributed cloud-native architectures, microservices, containerized applications, and hybrid multi-cloud ecosystems that demand advanced Site Reliability Engineering (SRE) practices. Traditional SRE approaches, while effective in static environments, struggle to address the growing challenges of predictive fault management, cybersecurity threats, automated incident response, and dynamic workload optimization. In response to these challenges, Artificial Intelligence (AI) and Machine Learning (ML) technologies have emerged as transformative tools capable of enhancing predictive reliability, operational intelligence, and secure automation in enterprise environments.This research article investigates advanced predictive AI frameworks designed for secure Site Reliability Engineering in enterprise systems. The study explores the integration of AI-driven anomaly detection, predictive analytics, reinforcement learning, deep learning, and autonomous remediation mechanisms within modern SRE pipelines. The article critically examines the limitations of traditional reliability engineering methodologies and evaluates how predictive AI enhances system observability, threat detection, fault prediction, and infrastructure resilience. Furthermore, the study presents a comparative analysis of AI-powered SRE frameworks, emphasizing their capabilities in proactive incident management, security intelligence, and adaptive scalability.The research methodology adopts a qualitative and analytical framework supported by literature review, comparative architectural evaluation, and case-based analysis from enterprise cloud platforms. Results indicate that predictive AI frameworks significantly improve system uptime, reduce Mean Time to Detect (MTTD) and Mean Time to Recover (MTTR), enhance cybersecurity resilience, and optimize operational efficiency. However, challenges such as model explainability, data privacy, computational overhead, algorithmic bias, and integration complexity remain critical concerns.The study concludes that AI-enabled secure SRE frameworks represent the future of intelligent enterprise infrastructure management. By integrating predictive intelligence with security-aware automation, organizations can achieve self-healing systems capable of maintaining operational continuity under dynamic and hostile digital environments.
Intelligent Enterprise Automation Using Agentic AI and Predictive Infrastructure Analytics
The rapid evolution of enterprise digital transformation has accelerated the adoption of intelligent automation technologies across modern organizations. Traditional automation systems, which rely heavily on predefined workflows and static rule engines, often struggle to address dynamic business environments characterized by large-scale data generation, infrastructure complexity, and real-time decision-making requirements. In this context, Agentic Artificial Intelligence (AI) and Predictive Infrastructure Analytics have emerged as transformative paradigms capable of enabling autonomous enterprise operations, adaptive orchestration, and predictive decision intelligence. This research article explores the integration of Agentic AI with predictive infrastructure analytics to establish intelligent enterprise automation frameworks capable of self-learning, autonomous coordination, and proactive operational optimization. The study examines how autonomous AI agents interact with enterprise systems to monitor infrastructure behavior, detect anomalies, predict failures, and optimize business processes without extensive human intervention. The research further analyzes the role of machine learning, reinforcement learning, generative AI, and predictive analytics in enhancing infrastructure resilience, operational efficiency, and business continuity. Comparative evaluations between traditional automation systems and AI-driven intelligent enterprise frameworks are presented to demonstrate the advantages of adaptive automation models. The proposed framework emphasizes intelligent orchestration, predictive maintenance, infrastructure observability, and autonomous remediation mechanisms. The findings indicate that enterprises implementing Agentic AI-based automation architectures achieve significant improvements in operational scalability, predictive accuracy, infrastructure reliability, resource optimization, and incident response efficiency. The article also identifies major implementation challenges including data privacy, explainability, governance complexity, and computational overhead. The study concludes that intelligent enterprise automation represents a foundational pillar for next-generation digital enterprises and highlights future research directions involving cognitive orchestration, multi-agent collaboration, and self-healing infrastructures.
Monitoring and Root Cause Analysis for Database Performance Issues
Database systems must manage a number of different workloads, distributed architectures as well as intense performance demands in the modern digital era. Companies are using data in increasing amounts to make these kinds of decisions, so even little drops in the efficiency of databases can be terrible for business. This study stresses the necessity of careful monitoring when sophisticated root cause analysis to make certain the databases work properly & are more reliable. Most standard tracking devices just give warnings after an incident has gone wrong and don't give comprehensive diagnosis information. This makes it tougher to resolve things and takes extra time to do so. The proposed method solves these kinds of problems by using continuous performance monitoring, anomaly detection along with correlation analysis to find performance regressions and bottlenecks in actual time. The system sets an ever-changing performance foundation by looking at things like query latency, resource use, and payment throughput. This makes it easier to find those issues early and figure out what's contributing to them on your own. The method uses algorithmic learning for predictive evaluation, which means it can generate predictions about trends along with improvements before they happen. The results of the analysis show that the system is now considerably more stable, it takes a shorter time to fix problems, and it has become simpler to find bugs than it was via conventional reactive monitoring. The results show just how crucial it is to employ observability, automation, and analytics in combination to make database management of performance a proactive and smart practice compared to a reactive one. This study strengthens the field of data structure optimization by presenting a scalable, analytics-driven scheme that empowers administrators to guarantee consistent performance, reduce downtime, and further improve customer satisfaction in dynamic database-based systems.
Reducing Outages with Proactive Monitoring and Alerting Systems
In a world that is becoming more and more digital, and where businesses are highly dependent on online services that need to be uninterrupted, the main concern of companies has been to ensure system uptime. The latter has become crucial for the maintenance of trust, productivity, and user satisfaction. Short shutdowns, even of a few minutes, may cause losses of money, interruption of the flow of work, and bad brand credibility thus a very high need for smarter and more proactive system management emerges. As such, this research proposes a proactive monitoring and alerting framework that is intended to first of all detect anomalies, secondly predict potential failures, and finally initiate preventive actions automatically if an outage is to happen at any time in the near future. Almost none traditional reactive models that only respond after downtime strikes are in fact in use; however, this approach goes far beyond that by incorporating predictive analytics, real-time performance metrics, and intelligent alerting mechanisms which help in the early identification and mitigation of the issues even before downtime is caused. The Effects Study that was carried out in a diverse environment of heavy-traffic, was able to prove through the evidence that very unplanned downtime has been remarkably reduced by a figure of over 40% besides of the faster incident resolution and better resource utilization. The results of this research work show power of continuous monitoring, along with automated alerting and adaptive response systems, in upgrading operational reliability. Along with the technical advantages, the framework helps in building a culture of accountability and responsiveness that are two characteristics IT teams possess when they make data-driven decisions and service management is handled proactively rather than reactively. To sum up, proactive monitoring is not only a device but a business planning instrument that is needed in order to be able to guarantee business continuity in a digital ecosystem which is characterized by constant availability.
Agile Development in Practice: From Intern to Contributor
Agile development has changed how modern software teams work by putting more emphasis on being more flexible, working together & always becoming better. This article talks about the main principles of Agile techniques, such as iterative development, flexible planning & delivery that focuses on the client. These are all important for effective software engineering. It shows how important it is for engineers to start adopting these Agile methods early on in their careers, when the best way to learn is by doing. Moving from intern to active contributor in Agile teams requires not just a change in skills but also a shift in how you think. You can't simply follow any orders; you have to actively participate in these sprints, retrospectives & many cross-functional problem-solving exercises. This article describes systematic ways to help new people fit into these Agile settings, including mentoring & progress tracking using metrics like velocity, quality & the cooperation. These methods are based on actual life case studies & genuine team experiences. The findings demonstrate that interns who worked with more than one Agile framework are more adaptable, better at communicating & have a better understanding of their shared duties. As kids become previous & begin to contribute, they learn to always provide value & be more flexible. This article says that Agile development improves their technical skills & builds resilience, curiosity & cooperation, which are all important traits for success in the present day's software industry.
The Collapse of Predictability in Large-Pool Redundant Storage
Hyperscale data centers have grown so fast that storage architecture has drastically changed from being isolated, well-defined systems to those extremely large, interconnected pools composed of thousands of drives, controllers, and failure domains. Redundancy mechanisms including replication and erasure coding have been the main ways to ensure reliability, but their example at such a scale is not clear anymore. This paper is about the unpredictability of large-pool redundant storage systems which grow so much that the traditional assumptions about independent failures and linear scaling no longer hold. With the expansion of storage infrastructures, there is a growing influence of correlated failures, repair traffic amplification, rebuild contention, and thermal or power-domain coupling on the overall dynamics, thus eliminating the possibility of using deterministic methods for capacity planning and availability forecasting. The main point which this paper tries to illustrate is that when the scale and redundancy density go beyond a certain limit, it is no longer possible to sum up the individual components of the system's reliability profile, but it becomes a property of the whole system, that is to say, increasing redundancy does not correspond to an increase in resilience and, it is even possible that systemic risk is heightened. In order to confirm this discovery, the authors rely on analytical reliability modeling in conjunction with large-scale simulation and patterns of real-world operational telemetry to measure failure propagation, rebuild times, and resource contention under various redundancy configurations. Some effects that contribute to failure clustering and repair amplification have been studied through the help of mean-time-to-data-loss predictions, while background recovery traffic is responsible for fluctuations in performance stability and these effects only serve to worsen one another under stress.
Agents, LLMs, and Salesforce with Multi-Cloud Provider (MCP)
This research paper explores a three-way convergence of large language models (LLMs), intelligent agent architecture, and the Salesforce ecosystem in the context of a multi-cloud provider (MCP) environment. Considering the modern distributed infrastructure scenario, which is spread out across AWS, Azure, and Google Cloud, the integration of Salesforce with LLM-powered agents is just revolutionary, as it opens up a new universe of possibilities for contextual reasoning, automation, and decision intelligence. The authors explain how AI-powered agents can profoundly change the way the CRM system functions as they become agile, self-learning platforms that are able to automate workflows, produce predictive insights and manage the flow of data effortlessly across different clouds. By placing LLMs at the core of Salesforce operations, companies can know their customers even better through natural language reasoning, instant sentiment understanding, and communication based on the customer's intent, thus surpassing the use of static rule-based logic. What is distinctive about this approach is the construction of a harmonized MCP model where autonomous agents can intercommunicate in different atmospheres, thereby securing, scaling, and enabling interoperability without the performance being affected. The research provides examples of what becomes feasible when LLMs are combined with Salesforce APIs and cloud-native tools; thus, intelligent automation can be done efficiently customer engagement through smart automation is the result of lead nurturing to service case resolution while operational friction is minimized. The major points of the paper uncover significant advances in productivity, data integrity, and cross-platform flexibility. Such high-level research opens the door to questions about the next steps of federated AI governance, ethical data handling, and the evolution of agentic frameworks capable of learning, reasoning, and collaborating within multi-cloud ecosystems, leading to the rise of the enterprise landscape that is more intelligent, connected, and human-centered.
Creating Object Quick Actions with Lightning Web Components
The Salesforce Lightning platform has changed the way businesses develop, customize, and deliver user experiences by introducing a new, component-driven framework that greatly increases productivity. The move from Aura Components to Lightning Web Components (LWCs) has, over time, been the major change as far as performance, modularity, and scalability of Salesforce development are concerned. LWCs use native web standards; thus, developers can easily create components that are responsive, lightweight, and reusable, and at the same time, these components can be effortlessly embedded in the Lightning Experience. Object Quick Actions, being the essential element of the constantly changing environment, are extensively involved in the optimization of the workflow processes by the means they give the users the opportunity to carry a limited number of key operations out by themselves (e.g. making, changing, or logging records) directly from record pages, and that is done without the necessity of going through numerous screens. This document discerns the reasons behind and the ways of routing the business with Object Quick Actions and Lightning Web Components, which leads to the simplification of the processes and the growth of the user's efficiency level. It also touches upon the best methods used in creating user-friendly layouts, handling data operations via Apex controllers, and facilitating uninterrupted communication between client-side LWCs and server-side logic. On top of that, by having a synergy between the technical deployment and the real-life scenarios, the integration is a demonstration of how routine operations can be revolutionized by Salesforce admins and developers so as to be executed seamlessly with a single click. In addition, the very nature of LWCs, which is modular, assures their scalability thus, Quick Actions can be not only spread over different objects but also various business units with the least possible amount of work in this way, organizations are granted the possibility of expansion.
How to Open a Modal Using Quick Action on the Record Detail Page
This article elaborates on a well-functioning and user-friendly technique of modal dialog window opening by means of Quick Actions from a record detail page, mainly concentrating on Salesforce Lightning and other similar component-based ecosystems. The call for such a method is due to the ever-increasing need for more beautiful interfaces, less navigation, and more natural user interactions. Modal windows in direct invocation from a Quick Action give users the freedom to execute simple procedures like generating related records, editing data, or proceeding through a guided workflow without the inconvenience of redirection to different pages. The gist of the work is the creation of a Quick Action which can call a custom component or a standard modal wrapper thereby empowering the teams to tailor the user experience according to particular business needs. The enactment is not only about the Quick Action set up and the creation of a component that is capable of dealing with modal logic but also about enabling the modal and the parent record page to have a smooth communication link. Different means of data transfer, validation, and event-driven updates are thought through to a great extent so that the interaction flow is not interrupted and is also prompt. The testing and refining stage, which is quite important here, is being carried out as it provides modal loading in a quick manner, the same functionality on various devices, and the accessibility standards being ​‍​‌‍​‍‌met. The main points are the considerably enhanced efficiency of workflows, the lesser tiredness of users due to frequent changing of pages, and the better understanding when performing tasks in a modal setting. Moreover, the application of this design pattern results in better management as it wraps the logic in reusable components, thus it becomes more effortless for the teams to take care of and develop further functionalities as the processes change.
Unobservable Performance: Storage Failures That Leave No Metrics Behind
Modern computer systems increasingly rely on intricate storage stack layers, and the performance of these layers can fail in ways that are not always easy to tell from error reports or measurable faults. A new category of silent or unobservable storage performance failures even trend to the point of causing the degradation of application throughput, latency, and reliability but without conventional alerts, counters, or health metrics being triggered. The root of these failures is the subtle coordination between the firmware, the controllers, caching layers, and the operating system which leave the system administrators little or no visibility into the real reason for the performance anomalies. Traditional monitoring frameworks largely rely on explicit error reporting, averaged latency metrics, or device-level statistics, which most of the time cannot pick up on transient stalls, internal throttling, or firmware-induced behaviours that are below the interface of the device and not directly observable. This paper tackles the problem of how to detect and characterize invisible performance failures which it does by offering a method that integrates workload-aware probing, cross-layer correlation, and anomaly-driven inference to bring to light hidden storage inefficiencies. Instead of just relying on the device-reported metrics, the method makes use of the end-to-end application behaviour, temporal patterns, and indirect performance signatures to deduce the storage pathologies. The real-life example in the paper shows a production system that was severely degraded in terms of its performance while its health indicators showed that it was in normal state. Later, it was confirmed that the problem was a silent internal storage bottleneck through the diagnostic procedure that was carried out by the proposed technique. Their studies expose the fact that system performance can deteriorate significantly without being noticed compromise because that the loss is undetected for a very long period which thus leads to misdiagnosis, poor scaling decisions, and reduced system reliability.
Immutability as an Operational Constraint Rather Than a Security Feature
Immutability is being more and more identified as a fundamental trait of contemporary digital systems right from simple append-only logs and object storage to distributed ledgers and infrastructure-as-code. And it is mostly touted as a security measure in a very narrow sense that only by making tampering, preserving evidence, and trust enforcement in a hostile environment physically impossible can the security feature be justified. We think that such a portrayal is not only partial but, as a matter of fact, in many cases, far from the truth and we disprove it here by a thorough exposé of the issue. Our alternative proposition is that immutability should primarily be regarded as an operational design constraint, i.e. one that determines the very nature of how the systems are built, handled and recovered over time. We carry out, through a conceptual dissection buttressed with an empirical study, a thorough examination of the detrimental consequences of organizations blindly deploying immutable components without the necessary insight into what these components actually imply at the operational level. The analysis reveals that the principle of immutability brings about alterations in failure modes thereby generating new problems rather than doing away with the old ones: it is simply impossible to rectify errors "in situ, " the operation of rollback loses its simplicity, and the mistakes get duplicated through the execution of replication along with automation. The case study depicts the dual effects of immutable logs and storage that, on the one hand, enhance auditability but, on the other hand, pose difficulties of retention, cost escalation, slow incident response, as well as the challenge of governance of corrective actions. Major outcomes are along the lines of indicating that the major resultant consequences of immutability are felt in lifecycle management, versioning, reconciliation, and recovery workflows, thus it is not a matter of threat prevention per se.
What the Jenkins Logs Won’t Tell You: Using an AI Agent to Capture the Lost ‘Bank Memory’ Behind a 76% Sprint Velocity Gain and Whether Another Community Bank Can Borrow It Without the Original Team
Community banks are increasingly adopting enterprise-scale FinTech backbones, but replicating a success story like a 76% jump in sprint velocity is rarely as simple as copying code or rerunning Jenkins pipelines. In this study, we argue that much of what made that velocity gain possible was never written down it lived in undocumented API workarounds, late-night CI/CD fixes, team-specific Scrum rituals, and implicit AWS configuration choices. We call this hidden asset lost bank memory. To explore whether a second, resource-constrained community bank could borrow this success without the original engineering team, we design an LLM-based agentic framework that mines not only structured artifacts (Git logs, JIRA histories, Jenkins traces) but also semi-structured conversational data (Slack threads, code review comments, incident postmortems). Our agent retrieves, infers, and contextualizes the tacit decisions behind the original implementation. Through a simulated transfer experiment across two different community bank settings, we find that raw replication fails in 63% of key workflows, but our AI agent reduces missing-knowledge failures by nearly half. The paper concludes that lost bank memory is a measurable, recoverable asset but only if we stop treating Jenkins logs as the whole truth.
Production Deployment of Computer-Aided Detection Systems in Mammography Screening: Throughput, False Positive Reduction, and Clinical Workflow Integration
Computer-aided detection systems for mammography have been part of clinical practice since the late 1990s, with the first generation FDA-cleared in 1998 and rapid adoption across United States screening facilities reaching a substantial majority by the mid 2010s. The first generation systems were associated with concerns about increased false positives and unclear effects on diagnostic accuracy in real-world deployment. The current generation, based on deep learning and rebranded by some vendors as AI-CAD to distinguish it from the classical CAD that preceded it, has produced results in observational studies that suggest meaningfully better diagnostic performance, although the long-term effect on patient outcomes remains the subject of ongoing prospective trials. This paper describes the production deployment of a mammographic prediction system that processed more than five thousand images per week, flagged suspicious anomalies for radiologist review, and contributed to a ten percent reduction in patient recall rates in the deployment setting. The paper focuses on the engineering decisions that determine whether such a system delivers its potential in production: throughput design, integration with the radiology workflow including PACS and reporting, false positive management, alert formatting to support rather than disrupt radiologist judgment, and the ongoing monitoring required to detect performance drift. The paper closes with a discussion of the regulatory environment, the limitations of single-site observational deployment as evidence, and the ongoing prospective trial work that is needed to confirm long-term outcomes.
AI-Powered Bug Triage Using Retrieval-Augmented Generation: A Weighted Confidence Scoring Approach with AWS Bedrock and Vector Search
Manual triage of incoming bug reports in a graphics engineering organization is expensive. A senior engineer reads the report, retrieves prior issues that look similar, consults architecture documentation, searches the source code for the relevant components, and produces an opinion on what the root cause is likely to be and which team should own the fix. This work commonly takes hours per report. This paper describes an AI bug triage agent that performs an equivalent analysis automatically and produces a structured root cause hypothesis with a confidence score. The agent is built on AWS Bedrock using Anthropic Claude as the language model. It uses a Retrieval-Augmented Generation pipeline grounded in a curated knowledge base of one thousand one hundred or more previously resolved issues, together with architecture documentation and source code search. It analyzes crash logs attached to the bug report when crash logs are present. The output is a root cause analysis with a confidence score derived from a weighted combination of five signals: historical pattern match against the knowledge base of resolved issues, source code match against the components implicated by the report, crash stack analysis, log evidence, and fix ownership. The weights adjust dynamically based on which signals are available for a given report. A Flask web dashboard exposes real-time triage status, analytics, filterable history, and routing views for issues that fall outside the team scope. Deployment of the agent reduced manual triage latency from hours to minutes for the cases the agent handles end to end, while preserving analyst trust through transparent and inspectable confidence scoring. The paper describes the architecture, the scoring algorithm, the dashboard, and the operational discipline that keeps the agent useful.
Deploying TensorFlow-Based Risk Assessment Models for High-Stakes Operational Decisions in Regulated Enterprise Systems: An Empirical Study of Lifecycle, Serving, and Drift Governance
Risk assessment models increasingly mediate consequential operational decisions in regulated enterprise environments, where accountability, auditability, and fairness constraints amplify the cost of silent model failure. This paper presents an empirical study of deployment patterns for TensorFlow-based risk-assessment models, synthesizing observations from the production deployments we examined across regulated enterprise operational systems. We describe a reference lifecycle that spans the feature pipeline, training discipline, validation protocol, TensorFlow Serving inference architecture, drift detection regime, and fairness audit cadence, and we evaluate each stage against criteria observed to matter most for high-stakes operational use. The measurement protocol couples temporally split holdout evaluation with calibration analysis, p95 inference latency measurement under autoscaling, and a quarterly fairness audit on illustrative protected groupings. Across the reference deployments, the production model achieved an observed AUC of 0.85, a Brier score of 0.12, an expected calibration error of 0.02, and a p95 inference latency of 96 ms under representative load. Illustrative fairness metrics across three protected groupings remained within a demographic-parity gap of 0.06 and an equal-opportunity gap of 0.04 after recalibration. We discuss how training-serving symmetry, calibrated outputs, and continuous drift monitoring jointly determine whether risk-assessment models retain operational trust over time, with implications for the broader field of trustworthy machine learning in regulated decision support.
Autonomous Component Lifecycle Management in Salesforce LWC using AI-driven Predictive Rendering
Making use of an AI-based predictive rendering system, this paper investigates how Salesforce Lightning Web Components (LWC) can develop better (more autonomous and efficient) lifecycle management. On one hand, modern Salesforce applications require very fast user interfaces, and on the other, traditional LWC rendering cycles typically depend on manual optimization and thus bring unnecessary re-renders, increased computational load, and inconsistent performance of complicated component hierarchies. To surpass these issues, the research presents an innovative way of lifecycle management that utilizes subtle machine learning signals to forecast component state changes even before the changes take place, and thus, rendering is only carried out when significant updates are present. Tests run on a prototype component in a Salesforce environment have achieved noticeable improvements in render frequency reduction, component responsiveness, and platform resource usage, especially in data-intensive and event-driven UIs. A case study of a multi-layer dashboard application also demonstrates that predictive rendering can, without human intervention, optimize the balancing between accuracy and performance, thereby limiting over-rendering without losing UI fidelity. The results indicate that the inclusion of AI-driven heuristics into LWC lifecycles not only results in better performance but also makes it easier to develop because the development team can move away from manual tuning and toward adaptive, self-regulating behavior. This research provides a viable architectural model, offers implementation guidelines, and presents empirical data that supports the concept of predictive rendering operating in the Salesforce environment. The impact of this work is not limited to the Salesforce scope but could lead to the development of intelligent LWCs that adapt to user behavior, thus increasing scalability and paving the way for additional self-governing user interface paradigms in cloud-based enterprise applications.
Multi-Language Dynamic UI Generation in Salesforce LWC using Metadata-driven Design
This​‍​‌‍​‍‌ article presents a metadata-driven technique to construct dynamic and multilingual user interfaces in Salesforce Lightning Web Components (LWC), which is in line with the increasing demand for versatile, worldwide accessible applications that do not cause any trouble for the different user groups. The standard treatment of multi-language requirements in Salesforce is through the use of hard-coded labels, repetitive configuration, or highly customized components, which in turn, escalate the problems of scaling, maintaining, and governing across rapid business changes. To prevail over the obstacles, the solution put forward here, inter alia, plans to systematize UI layout, field definitions, language labels, and behavioral rules in such a way that they could be identified from custom metadata and custom settings, thereby allowing LWCs to define their form and content dynamically in real time. The process features an outline for a metadata schema for UI configuration, the construction of the Apex service layer and Lightning Data Service, and the realization of a universal LWC engine, which is capable of understanding metadata instructions to create forms, sections, validation logic, and multilingual labels instantaneously. This UI transformation architecture removes coding from UI behavior, thereby reducing the chances of duplication, simplifying localization, and granting screen modification rights to administrators without the need for development cycles. Experimentation outcomes suggest that new language onboarding, deployment overhead, and consistent UI experiences across regions have all been significantly streamlined. The methodology also convinces that its conformity with Salesforce’s low-code principles is robust as it transfers the control from custom code to configurable metadata. Among the innovations of the present project are a workable map for metadata-driven UI generation, a facile LWC rendering engine, and a well-organized model for managing multilingual content, which, in turn, boosts the agility of worldwide Salesforce implementations.
AI-Enhanced Process Automation using Workato and Salesforce Einstein for Service Case Management
Nowadays, digital transformation in service operations is more and more aimed at meeting the customers' needs for speed, consistency and a better overall experience. However, traditional service case management is still a big challenge because it is basically a very manual operation supported by disconnected systems and rule-based workflows, all of which together cause inefficiencies and delays. This paper proposes an AI-augmented process automation technique for service case management via Workato and Salesforce Einstein. Workato is used as an Integration Platform as a Service (iPaaS) to orchestrate cross-system workflows, while Salesforce Einstein is the embedded AI engine that makes the case classification, prioritization, sentiment analysis, and action recommendations more smart and intuitive. The suggested framework is a great solution for the problems of inconsistent case handling, slow response times, and lack of operational visibility by mixing Einstein's predictive intelligence with Workato's event-driven automation to facilitate automated case intake, intelligent routing, automatic escalation, and continuous learning. A case from real life shows that there are sizable improvements in first-response time, resolution time and agent productivity, whilst at the same time maintaining governance and scalability. The paper is a tool that brings a doable technical architecture, operational best practices, and strategic insights for those organizations that want to use AI-driven automation as the path to digital transformation and modernization service case management accelerating enterprise-wide.
Federated Micro Frontend Governance in Enterprise Retail Ecosystems
Today’s retail enterprises need scalable and modular frontend architectures to support omnichannel commerce, global operations, and rapidly changing customer expectations. Traditional monolithic frontend systems often lack deployment flexibility, agility, and team autonomy. Federated Micro Frontend (FMF) architecture addresses these issues by applying microservices principles to frontend systems, enabling decentralized ownership, independent deployment, and faster innovation. However, FMF introduces governance challenges such as architectural consistency, security enforcement, dependency management, compliance control, metadata standardization, performance optimization, and organizational coordination. Without proper governance, organizations may face fragmentation, duplicated functionality, inconsistent user experience, and technical debt. This paper proposes a holistic governance framework for Federated Micro Frontends in enterprise retail ecosystems. It focuses on key governance areas including architectural governance, runtime orchestration, dependency governance, security governance, metadata governance, data quality governance, and operational governance. The study also introduces governance maturity metrics such as deployment autonomy, component reusability, compliance rate, release frequency, defect density, and user experience consistency. The research evaluates modern technologies like Webpack Module Federation, CI/CD automation, container orchestration, policy-as-code frameworks, zero trust security, AI-driven anomaly detection, and metadata intelligence systems. Special attention is given to retail-specific requirements such as omnichannel synchronization, promotional campaign management, dynamic pricing, catalog synchronization, and regional customization. Findings indicate that organizations adopting governed federated architectures achieve significant improvements, including over 60% reduction in deployment cycle time, 45% increase in component reuse, and more than 90% governance compliance. The study concludes that governance-driven federated micro frontend architecture is not only a technical necessity but also a strategic capability for sustainable retail innovation, scalability, operational resilience, and enhanced customer experience.
A Microservices-Based Architecture for High-Performance Customer Relationship Management Applications
In today's digital age, where customer engagement is essential for business success, Customer Relationship Management (CRM) applications have emerged as vital tools for organizations aiming to optimize their business processes and decision-making. The traditional monolithic CRM system designs are often unable to fulfill the greater requirements of scalability, agility, maintainability, interoperability, and real-time data processing. With the recent trend of cloud-native technologies, distributed computing paradigms, and DevOps practices, microservices-based architectures are becoming a game-changing way to design high-performance CRM systems. This paper provides an extensive analysis of a microservices-based architecture suitable for high-performance CRM applications with the aim of pre-October 2023. The proposed architecture solves the key challenges of the existing conventional CRM system such as scalability bottlenecks, deployment complexity, dependency management for services and fault tolerance. The study looks at the shift from a monolithic enterprise system to a modular and service-based CRM system that can handle millions of concurrent customer interactions that span distributed infrastructures. It discusses the benefits of microservices in terms of independent deployment, decentralized data management, containerized execution environments, and continuous integration and continuous deployment (CI/CD) pipelines. Moreover, the paper points out the benefits of cloud orchestration technologies including Kubernetes, Docker, API gateways, service meshes, event-driven communication and distributed monitoring systems for enhancing the performance and reliability of CRM. An extensive literature survey is performed to analyze the current practices of adopting microservices in enterprise systems, customer analytics platforms, integration with distributed database systems, and scalable architectures of cloud-native systems. The survey reveals that interoperability, distributed transaction management, observability, data consistency, and security enforcement are some of the major research gaps with microservices-powered CRM ecosystems. The proposed methodology will implement a multi-layered microservice architecture with a focus on customer management services, lead tracking modules, sales automation engines, analytics services, authentication services, and messaging brokers. Its architecture enables horizontal scaling, asynchronous communication, resilience engineering and real-time customer insights. The study also analyzes the proposed architecture in terms of performance metrics like response time, throughput, fault tolerance, resource utilization, scalability efficiency, and deployment flexibility. Experimental results show that the processing efficiency and the operational reliability are considerably better than the monolithic CRM systems. The proposed system can guarantee lower service latency, higher service request handling, faster service deployment time, and better fault isolation. Furthermore, the architecture allows for enterprise-level integration with artificial intelligence (AI), machine learning (ML), Internet of Things (IoT) and big data analytics platforms. The paper reports on an architectural model that is highly extensible and resilient, and consistent with modern cloud-native principles, to advance scalable enterprise CRM infrastructures. The results highlight the potential of microservices-based CRM systems to meet the evolving needs of businesses, enhance customer satisfaction, streamline operations, and boost technological flexibility. The research is interesting for architects, enterprise developers, cloud engineers and organizational decision makers who are interested in modernizing CRM systems using distributed and service design approaches.
LLM-Augmented Conversational Intelligence for Customer Workflow Continuity
Large Language Models (LLMs) have proven to be revolutionary technologies that transform enterprise applications into intelligent, adaptive, and context-aware conversational systems. Conversational AI has become a growing part of the modern function of customer engagement platforms, helping to automate support tasks, save time on workflow execution, and offer better customer experiences through digital channels. Yet, there are contextual memory issues, a lack of workflow continuity, multi-session state management problems and database integration with enterprise processes in the current conversational systems. These restrictions cause unions to be inconsistent, inputs from customers to be repeated, disruptions to the workflow, reduced efficiency in operations, thus, and lower customer satisfaction. In order to solve these problems, this paper presents an LLM-Augmented Conversational Intelligence Framework, which aims to maintain contextual memory in customer workflows, intelligently orchestrate workflows with LLM, and adaptively make decision with LLM in customer interactions. The proposed framework combines transformer-based LLMs, with Retrieval-Augmented Generation (RAG), contextual memory repositories, workflow state management engines, and enterprise integration layers, enabling ongoing and context-aware interactions with customers via various communication channels. It's also built with AI-powered intent recognition, contextual state management, workflow recovery and real-time orchestration, all of which enhance the consistency and resilience of enterprise conversations. The research takes a hybrid approach, with simulated enterprise datasets, workflow interaction scenarios and comparative benchmarking with existing chat bot systems, rules-based architectures and traditional NLP conversational models. The proposed framework is evaluated using multiple metrics like context retention accuracy, workflow completion rate, response coherence, latency, customer satisfaction, intent detection accuracy and workflow recovery efficiency. The experimental results show that the LLM-based architecture markedly surpasses traditional conversational architectures in terms of contextual continuity, minimising disruptions to workflow, conversational coherence and efficiency of information delivery to customers. It presents a workflow continuity engine, persistent contextual memory management strategies and features for secure enterprise orchestration of customer-centric AI ecosystems and a scalable conversational intelligence architecture of its own. The results additionally centralize that a conversational solution powered by an LLM can significantly reshape enterprise customer interaction models by facilitating clever workflow automation, adaptive decision help, plus autonomous enterprise interaction. The future open research areas encompass studying federated conversational intelligence, multi-agent collaboration with LLM systems, explainable AI-led orchestration, conversational AI on the edge, and autonomous enterprise workflow ecosystems that can optimise themselves and proactively support customers.
Preparing Enterprise Data for LLM-Assisted Customer Issue Analysis: A Governance-Centric Framework
The increasing adoption of Large Language Models (LLMs) in enterprise environments has transformed customer support operations by enabling intelligent issue classification, automated response generation, and context-aware analytics. The effectiveness performance of LLM-powered customer issue analysis relies on the quality, governance, security, and compliance of enterprise data preparation pipelines, though. Organizations are still grappling with a host of issues, including poor record management, differing metadata, concerns about privacy, and compliance with regulations, that hinder the trustworthiness and scalability of AI-powered customer service. To address these gaps, this study introduces a framework centered on governance principles for preparing data for use by LLMs to analyses customer issues, incorporating all of the following aspects into a single analytical architecture data ingestion, cleansing, metadata management, compliance enforcement, data lineage tracking and privacy-aware data preprocessing. This framework also adopts governance-centric components, like access control, audit logging, anonymization, semantic enrichment and policy validation, to enable secure and explainable AI operations. The analytical performance has been shown to be improved by experimental evaluation over 10,000 enterprise customer support tickets, resulting in 92.3% classification accuracy and 0.91 F1-score in comparison to conventional ungoverned LLM approaches. The framework also lowered the chances of hallucination, boosted readiness for compliance and kept the inference latency low enough to meet the needs of real-time enterprise applications. The findings show that data preparation with governance awareness significantly contributes to the reliability, transparency, and scalability of LLM-powered customer service solutions. The suggested framework offers a pragmatic approach for the trustworthy enterprise AI adoption and secure, compliant, and efficient analysis of customer issues for modern digital organizations.